A method, apparatus and equipment for optimizing plasma spectral data parameters

Abstract

This invention discloses a method, apparatus, and device for optimizing plasma spectral data parameters, belonging to the field of plasma diagnostic technology. The method includes acquiring plasma spectral data and determining the wavelength sequence and spectral line intensity sequence of the plasma spectral data. A broadening effect function containing the plasma parameters to be optimized and the wavelength sequence is constructed, and a spectral line fitting profile is obtained based on the broadening effect function and the wavelength sequence. This application employs an iterative algorithm, using the plasma parameters to be optimized as the optimization target. The iterative algorithm is executed based on the spectral line fitting profile and the spectral line intensity sequence to finally obtain the optimized plasma parameters. The optimized plasma parameters have higher accuracy and better diagnostic performance for plasma.

Description

Technical Field

[0001] This application belongs to the field of plasma diagnostic technology, and in particular relates to a method, apparatus and equipment for optimizing plasma spectral data parameters. Background Technology

[0002] Plasma spectra are mainly line spectra and continuous spectra. Line spectra are produced when neutral atoms and ions in plasma transition from excited states at higher energy levels to lower energy levels. The intensity of spectral lines emitted by a single particle is mainly determined by: ① the probability that the outer electron of the atom or ion is in an upper energy level, ② the probability that this electron transitions from an upper energy level to a lower energy level, and ③ the probability that the photon is reabsorbed before escaping the plasma. However, the total intensity of spectral lines is related to the density and temperature of electrons and ions, and each spectral line has its own intensity distribution pattern. Therefore, by measuring the intensity of spectral lines, combined with theoretical models and atomic data from the above spectra, information such as the density and temperature of electrons and ions can be obtained. According to the Doppler effect, the macroscopic velocity of the plasma can be determined from the shift in spectral line wavelengths. Continuous spectra are produced when electrons are accelerated or decelerated in the potential field of other particles. Data such as electron density and temperature can also be obtained from measuring the intensity of continuous spectra.

[0003] Spectroscopic diagnostics is one of the widely used methods for plasma diagnostics. It analyzes parameters such as ion temperature and electron density of plasma based on the measurement of plasma spectral data. However, the plasma parameters measured by existing spectroscopic diagnostic methods have low accuracy and are difficult to meet diagnostic needs. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and device for optimizing plasma spectral data parameters, so as to overcome the problem that the accuracy of plasma parameters measured by the existing spectral diagnostic method is low and cannot meet the diagnostic requirements.

[0005] A method for optimizing plasma spectral data parameters, comprising:

[0006] Acquire plasma spectral data and determine the wavelength sequence and spectral line intensity sequence of the plasma spectral data;

[0007] Construct a broadening effect function that includes the plasma parameters to be optimized and the wavelength sequence;

[0008] The spectral line fitting profile is obtained based on the broadening effect function and the wavelength sequence;

[0009] Using the plasma parameters to be optimized as the optimization target, an iterative algorithm is executed based on the spectral line fitting profile and the spectral line intensity sequence;

[0010] Output optimized plasma parameters.

[0011] Preferably, in one feasible embodiment of this application, after acquiring the plasma spectral data, the method further includes:

[0012] The optical thickness of the plasma is determined, and the spectral line intensity sequence is corrected based on the optical thickness of the plasma.

[0013] Preferably, in one feasible embodiment of this application, determining the optical thickness of the plasma and correcting the spectral line intensity sequence based on the optical thickness of the plasma includes:

[0014] Based on the Abelian transform, the initial spectral line intensity is obtained by integrating the radius of the plasma.

[0015] Based on the inverse Abelian transform, the spectral line intensity distribution is obtained according to the initial spectral line intensity.

[0016] The absorption coefficient is determined based on the spectral line intensity distribution;

[0017] Based on the Abelian transform, the optical thickness at different locations of the plasma is determined according to the absorption coefficient.

[0018] Based on Beer-Lambert's law, the spectral line intensities at different locations of the plasma are corrected according to the optical thickness at different locations of the plasma, and the corrected spectral line intensities at different locations of the plasma are obtained.

[0019] Generate a spectral line intensity sequence.

[0020] Preferably, in one feasible embodiment of this application, after acquiring the plasma spectral data, the method further includes: normalizing the plasma spectral data.

[0021] Preferably, in one achievable embodiment of this application, the plasma parameters to be optimized include: ion temperature, electron density, and spectral line center wavelength;

[0022] The construction of the broadening effect function, which includes the plasma parameters to be optimized and the wavelength sequence, includes:

[0023] Construct an upsampled wavelength sequence of the wavelength sequence, wherein the upsampled wavelength sequence is bounded by the maximum and minimum values ​​of the wavelength sequence;

[0024] A Gaussian function is constructed based on the upsampling wavelength sequence, the ion temperature, and the center wavelength of the spectral line; the Gaussian function is used to represent the intensity of the Doppler broadened spectral line.

[0025] A Lorentz distribution function is constructed based on the upsampling wavelength sequence, the electron density, and the center wavelength of the spectral line; the Lorentz distribution function is used to represent the intensity of the Stark broadened spectral line.

[0026] Preferably, in one feasible embodiment of this application, obtaining the spectral line fitting profile based on the broadening effect function and the wavelength sequence includes:

[0027] Discrete convolution is performed on the Gaussian function, the Lorentz distribution function, and the upsampled wavelength sequence;

[0028] The discrete convolution result is interpolated in the wavelength sequence to obtain the spectral line fitting profile.

[0029] Preferably, in one feasible embodiment of this application, the step of executing an iterative algorithm based on the spectral line fitting profile and the spectral line intensity sequence, with the plasma parameters to be optimized as the optimization target, includes:

[0030] Calculate the least squares error of the spectral line fitting profile and the spectral line intensity sequence;

[0031] Using the plasma parameters to be optimized as the optimization target, and based on an iterative algorithm, the least squares error of the spectral line fitting profile and the spectral line intensity sequence is reduced until the iteration stopping condition is met.

[0032] Preferably, in one achievable manner according to this application, the iteration stopping condition is: satisfying the iteration stopping number or satisfying a preset least squares error value.

[0033] A plasma spectral data parameter optimization device, comprising:

[0034] The acquisition module acquires plasma spectral data and determines the wavelength sequence and spectral line intensity sequence of the plasma spectral data.

[0035] A construction module is used to construct a broadening effect function that includes the plasma parameters to be optimized and the wavelength sequence;

[0036] The fitting module is used to obtain the spectral line fitting profile based on the broadening effect function and the wavelength sequence;

[0037] The iterative module takes the plasma parameters to be optimized as the optimization target and executes an iterative algorithm based on the spectral line fitting profile and the spectral line intensity sequence.

[0038] The output module is used to output the optimized plasma parameters.

[0039] A plasma spectral data parameter optimization device, comprising:

[0040] Processor and memory;

[0041] The processor and memory are connected via a communication bus;

[0042] The processor is used to call and execute the program stored in the memory;

[0043] The memory is used to store a program, which is at least used to execute a plasma spectral data parameter optimization method as described in any of the above.

[0044] Compared with the prior art, the present invention has the following beneficial technical effects:

[0045] This invention provides a method for optimizing plasma spectral data parameters, including acquiring plasma spectral data and determining the wavelength sequence and spectral line intensity sequence of the plasma spectral data. A broadening effect function is constructed, incorporating the plasma parameters to be optimized and the wavelength sequence. A spectral line fitting profile is obtained based on the broadening effect function and the wavelength sequence. This application employs an iterative algorithm, using the plasma parameters to be optimized as the optimization target. The iterative algorithm is executed based on the spectral line fitting profile and the spectral line intensity sequence, ultimately obtaining optimized plasma parameters. The optimized plasma parameters have higher accuracy and better diagnostic performance for plasma. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a method for optimizing plasma spectral data parameters according to Embodiment 1 of the present invention.

[0047] Figure 2 This is an example diagram of performing an inverse Abelian transform in Embodiment 1 of the present invention.

[0048] Figure 3 This is a schematic diagram of a plasma spectral data parameter optimization device according to Embodiment 2 of the present invention.

[0049] Figure 4 This is a schematic diagram of a plasma spectral data parameter optimization device according to Embodiment 3 of the present invention.

[0050] In the diagram: Acquisition module-21; Construction module-22; Fitting module-23; Iteration module-24; Output module-25; Processor-31; Memory-32. Detailed Implementation

[0051] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] Example 1

[0054] Figure 1 This is a flowchart illustrating a method for optimizing plasma spectral data parameters according to an embodiment of this application. (Refer to...) Figure 1 A method for optimizing plasma spectral data parameters, comprising:

[0055] S11: Acquire plasma spectral data and determine the wavelength sequence and spectral line intensity sequence of the plasma spectral data;

[0056] Understandably, to simplify calculations, after acquiring the plasma spectral data, it is normalized to ensure that the maximum spectral intensity I is minimized. max =1, thus obtaining the wavelength sequence {λ i} and spectral line intensity sequence {I i}

[0057] It is understandable that since capillary discharge plasma is usually optically thick plasma, self-absorption may occur due to uneven distribution of plasma components and temperature. In this embodiment, the influence of plasma optical thickness on spectral line intensity is considered. After acquiring plasma spectral data, the optical thickness of the plasma is also determined, and the spectral line intensity sequence is corrected according to the optical thickness of the plasma.

[0058] This embodiment mainly uses the inverse Abelian transform to correct the spectral line intensity sequence. See [link / reference] Figure 2In practice, the initial spectral line intensity is obtained by integrating the radius of the plasma based on the Abelian transform.

[0059] Assuming the plasma has rotational symmetry, i.e., satisfies the cylindrical assumption, such as... Figure 2 The circularly symmetric region shown is represented by ε(r), which is the radiation coefficient at radius r. Integrating ε(r) along path S from point A to point B yields the initial spectral line intensity I. rad (y):

[0060]

[0061] For ease of calculation, the above equation is rewritten in the form where r is the integrating factor, i.e., the Abelian transform, denoted as .

[0062] Based on the inverse Abelian transform, the spectral line intensity distribution is obtained according to the initial spectral line intensity.

[0063] If the initial spectral line intensity I is known rad (y), solving for the radiation intensity distribution, this process is called the inverse Abelian transform, denoted as .

[0064]

[0065] Determine the absorption coefficient based on the spectral line intensity distribution;

[0066] Optical thickness and absorption coefficient κ of a specific wavelength spectral line at different radius positions λ Correlation, and absorption coefficient κ λ The relationship between radiation coefficient and radiation coefficient can be established using Planck's equation:

[0067]

[0068] In the formula κ λ ε λ and B λ The subscript λ represents a specific wavelength.

[0069] Based on the Abelian transform, the optical thickness at different locations of the plasma is determined according to the absorption coefficient.

[0070] Through the Abelian transform, the optical thickness at spatial location y for a specific wavelength can be defined as:

[0071]

[0072] Based on Beer-Lambert's law, the spectral line intensities at different locations of the plasma are corrected according to the optical thickness at different locations of the plasma, and the corrected spectral line intensities at different locations of the plasma are obtained.

[0073] According to Beer-Lambert's law, the corrected spectral line intensity and the initial spectral line intensity have the following relationship:

[0074]

[0075] in, The corrected spectral line intensity; The initial spectral line intensity.

[0076] Based on this, after obtaining the corrected spectral line intensities at different locations in the plasma, a spectral line intensity sequence can be further generated.

[0077] S12: Construct a broadening effect function containing the plasma parameters to be optimized and the wavelength sequence;

[0078] It should be noted that the plasma parameters to be optimized include: ion temperature, electron density, and spectral center wavelength;

[0079] Constructing a broadening effect function that includes the plasma parameters to be optimized and the wavelength sequence includes:

[0080] Construct an upsampled wavelength sequence from the wavelength sequence, with the maximum and minimum values ​​of the wavelength sequence as boundaries;

[0081] A Gaussian function is constructed based on the upsampling wavelength sequence, ion temperature, and spectral center wavelength; the Gaussian function is used to represent the intensity of Doppler broadened spectral lines.

[0082] The Lorentz distribution function is constructed based on the upsampling wavelength sequence, electron density, and spectral line center wavelength; the Lorentz distribution function is used to represent the intensity of the Stark broadened spectral lines.

[0083] It is understandable that the intensity of Stark's broadened spectral lines follows a Lorentz distribution:

[0084]

[0085] Among them, L max w represents the spectral line intensity amplitude. L λ is the Stark broadening (half-width at half maximum) of the Lorentz line in nm; λ0 is the center wavelength of the spectral line in nm.

[0086] The relationship between Stark broadening and electron density and electron temperature is as follows:

[0087]

[0088] Where ω is the electron collision parameter / s -1 ;n e Electron density / cm -3 α is the electrostatic ion broadening parameter; T e The value is the electron temperature in K.

[0089] In the above equation, the first term on the right-hand side represents the contribution of the electronic electric field, and the second term is the ion correction factor, representing the ion contribution. For non-hydrogen-like atoms (with only one electron in their outermost shell), Stark broadening is mainly affected by electrons, thus the above equation can be simplified to:

[0090] w L =2×10 -18 ωn e

[0091] Based on this, the Lorentz distribution function constructed from the upsampled wavelength sequence, electron density, and spectral line center wavelength is:

[0092]

[0093] It is understandable that the intensity of Doppler broadened spectral lines follows a Gaussian distribution:

[0094]

[0095] Among them, G max w represents the spectral line intensity amplitude. D λ0 represents the Doppler broadening of the Gaussian line (1 / e of the amplitude) / nm; λ0 is the center wavelength of the spectral line / nm.

[0096] The relationship between Doppler broadening and particle temperature T (K) is as follows:

[0097]

[0098] Where, λ nom Nominal center wavelength / nm; k B Boltzmann constant, 1.39064852(79)×10 -23 J.K. -1 m ion The value is the particle mass in kg; c is the speed of light, 299,792,458 m / s. -1 .

[0099] Based on this, the ion temperature (eV) for:

[0100]

[0101] Based on this, the Gaussian function constructed from the upsampling wavelength sequence, ion temperature, and spectral center wavelength is:

[0102]

[0103] S13: Obtain the spectral line fitting profile based on the broadening effect function and wavelength sequence;

[0104] It should be noted that when multiple broadening effects exist in the radiative plasma, the obtained spectral profile is the convolution of each broadening profile. Therefore, obtaining the spectral fitting profile based on the broadening effect function and wavelength sequence includes: performing discrete convolution on the Gaussian function, Lorentz distribution function, and upsampled wavelength sequence; and interpolating the discrete convolution result into the wavelength sequence to obtain the spectral fitting profile.

[0105] In practice, for typical capillary discharge plasmas, the main broadening effects are Stark broadening and Doppler broadening, and the spectral profile produced by convolution is a Voigt (Kelvin) shape.

[0106]

[0107] Among them, A v The effective strength of the Voigt line type, i.e., G max and L max The product of; let u1, u2, and u3 represent the ion temperature T, electron density n, and so on. e And the center wavelength λ0 of the spectral line.

[0108] right and Perform discrete convolution, and use u4 to represent the magnitude after convolution.

[0109]

[0110] in,

[0111] Let V(k) be in {λ i Interpolation is performed within} to obtain V fit and V fit As a profile for fitting Voigt line-type spectral lines.

[0112] S14: Using the plasma parameters to be optimized as the optimization target, an iterative algorithm is executed based on the spectral line fitting profile and the spectral line intensity sequence;

[0113] An iterative algorithm is executed based on the spectral line fitting profile and the spectral line intensity sequence, including: calculating the least squares error of the spectral line fitting profile and the spectral line intensity sequence; using plasma parameters as the optimization objective, the least squares error of the spectral line fitting profile and the spectral line intensity sequence is reduced based on the iterative algorithm until the iteration stopping condition is met.

[0114] During implementation, the iteration stopping condition is: either the iteration stopping number is met or the preset least squares error value is met.

[0115] The formula for calculating the least squares error of the spectral line fitting profile and the spectral line intensity sequence is as follows:

[0116]

[0117] Preferably, in this embodiment, the least squares error is reduced by iterative calculation based on the unconstrained optimization method of the variable simplex method, so as to optimize the values ​​of u1, u2, u3 and u4.

[0118] S15: Outputs optimized plasma parameters.

[0119] In this embodiment, after the iteration stops, the final optimized plasma parameters u1 and u2 are output, namely ion temperature and electron density.

[0120] This embodiment presents a method for optimizing plasma spectral data parameters, comprising: acquiring plasma spectral data and determining the wavelength sequence and spectral line intensity sequence of the plasma spectral data; constructing a broadening effect function containing the plasma parameters to be optimized and the wavelength sequence; and obtaining a spectral line fitting profile based on the broadening effect function and the wavelength sequence. This embodiment employs an iterative algorithm, using the plasma parameters to be optimized as the optimization target, and executing the iterative algorithm based on the spectral line fitting profile and the spectral line intensity sequence to finally obtain optimized plasma parameters. The optimized plasma parameters have higher accuracy and better diagnostic performance for plasma.

[0121] Example 2

[0122] Figure 3 This is a schematic diagram of a plasma spectral data parameter optimization device according to an embodiment of this application, with reference to... Figure 3 A plasma spectral data parameter optimization device, comprising:

[0123] The acquisition module 21 acquires plasma spectral data and determines the wavelength sequence and spectral line intensity sequence of the plasma spectral data;

[0124] Module 22 is used to construct a broadening effect function that includes the plasma parameters to be optimized and the wavelength sequence;

[0125] Fitting module 23 is used to obtain the spectral line fitting profile based on the broadening effect function and wavelength sequence;

[0126] Iteration module 24 takes the plasma parameters to be optimized as the optimization target and executes an iterative algorithm based on the spectral line fitting profile and spectral line intensity sequence.

[0127] Output module 25 is used to output optimized plasma parameters.

[0128] This embodiment of a plasma spectral data parameter optimization device includes: an acquisition module 21, a construction module 22, a fitting module 23, an iteration module 24, and an output module 25. In implementation, the acquisition module 21 acquires plasma spectral data and determines the wavelength sequence and spectral line intensity sequence of the plasma spectral data. The construction module 22 constructs a broadening effect function containing the plasma parameters to be optimized and the wavelength sequence. The fitting module 23 obtains the spectral line fitting profile based on the broadening effect function and the wavelength sequence. The iteration module 24 employs an iterative algorithm, using the plasma parameters to be optimized as the optimization target, and executes the iterative algorithm based on the spectral line fitting profile and the spectral line intensity sequence to finally obtain the optimized plasma parameters, which are then output through the output module 25. The optimized plasma parameters have higher accuracy and better diagnostic performance for plasma.

[0129] A plasma spectral data parameter optimization device further includes:

[0130] The correction module is used to determine the optical thickness of the plasma and correct the spectral line intensity sequence based on the plasma's optical thickness. Specifically, it integrates the plasma radius based on the Abelian transform to obtain the initial spectral line intensity; it obtains the spectral line intensity distribution based on the initial spectral line intensity based on the inverse Abelian transform; it determines the absorption coefficient based on the spectral line intensity distribution; it determines the optical thickness at different locations of the plasma based on the absorption coefficient based on the Abelian transform; it corrects the spectral line intensities at different locations of the plasma based on the Beer-Lambert law, obtaining the corrected spectral line intensities at different locations of the plasma; and it generates the spectral line intensity sequence.

[0131] The normalization module is used to normalize plasma spectral data.

[0132] Example 3

[0133] Figure 4 This is a schematic diagram of a plasma spectral data parameter optimization device according to an embodiment of this application, with reference to... Figure 4 A plasma spectral data parameter optimization device, comprising:

[0134] Processor 21 and memory 22;

[0135] The processor 21 and the memory 22 are connected via a communication bus;

[0136] The processor 21 is used to call and execute the program stored in the memory 22;

[0137] The memory 22 is used to store a program, which is at least used to execute a plasma spectral data parameter optimization method as described in the above embodiments.

[0138] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0139] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0140] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0141] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0142] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0144] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0145] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method of optimizing plasma spectral data parameters, characterized by, The method comprises the following steps: acquiring plasma spectrum data and determining a wavelength sequence and a spectral line intensity sequence of the plasma spectrum data; after acquiring the plasma spectrum data, determining an optical thickness of the plasma, and correcting the spectral line intensity sequence according to the optical thickness of the plasma; the step of determining the optical thickness of the plasma and correcting the spectral line intensity sequence according to the optical thickness of the plasma comprises the following steps: integrating the radius of the plasma based on an Abel transformation to obtain an initial spectral line intensity; obtaining a spectral line intensity distribution based on an inverse Abel transformation according to the initial spectral line intensity; determining an absorption coefficient according to the spectral line intensity distribution; determining the optical thickness of the plasma at different positions based on the Abel transformation according to the absorption coefficient; correcting the spectral line intensity at different positions of the plasma based on the optical thickness of the plasma at the different positions according to the Beer-Lambert law to obtain the corrected spectral line intensity at the different positions of the plasma; generating a spectral line intensity sequence; constructing a broadening effect function containing a to-be-optimized plasma parameter and the wavelength sequence; the to-be-optimized plasma parameter comprises an ion temperature, an electron density and a spectral line center wavelength; the step of constructing the broadening effect function containing the to-be-optimized plasma parameter and the wavelength sequence comprises: constructing an up-sampling wavelength sequence of the wavelength sequence, the up-sampling wavelength sequence being bounded by a maximum value and a minimum value of the wavelength sequence; constructing a Gaussian function according to the up-sampling wavelength sequence, the ion temperature and the spectral line center wavelength, the Gaussian function being used to represent a Doppler broadening spectral line intensity; constructing a Lorentz distribution function according to the up-sampling wavelength sequence, the electron density and the spectral line center wavelength, the Lorentz distribution function being used to represent a Stark broadening spectral line intensity; obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence; performing an iterative algorithm according to the spectral line fitting profile and the spectral line intensity sequence with the to-be-optimized plasma parameter as an optimization target, and outputting an optimized plasma parameter.

2. The method of claim 1, wherein, after acquiring the plasma spectrum data, normalizing the plasma spectrum data.

3. The method of claim 1, wherein: the step of obtaining a spectral line fitting profile according to the broadening effect function and the wavelength sequence comprises: performing discrete convolution on the Gaussian function, the Lorentz distribution function and the up-sampling wavelength sequence; performing interpolation on the discrete convolution result in the wavelength sequence to obtain the spectral line fitting profile.

4. The method of claim 1, wherein, the step of performing an iterative algorithm according to the spectral line fitting profile and the spectral line intensity sequence with the to-be-optimized plasma parameter as an optimization target comprises: calculating a least square error of the spectral line fitting profile and the spectral line intensity sequence; based on the iterative algorithm, reducing the least square error of the spectral line fitting profile and the spectral line intensity sequence with the to-be-optimized plasma parameter as an optimization target until an iterative stop condition is met.

5. The method of claim 4, wherein: the iterative stop condition is that an iterative stop number is met or a preset least square error value is met.

6. A plasma spectroscopic data parameter optimization apparatus for use in the plasma spectroscopic data parameter optimization method according to claim 1, characterized by, the method comprises the following steps: An acquisition module acquires plasma spectrum data and determines a wavelength sequence and a spectral line intensity sequence of the plasma spectrum data; A construction module is configured to construct a broadening effect function containing a plasma parameter to be optimized and the wavelength sequence; A fitting module is configured to obtain a spectral line fitting profile according to the broadening effect function and the wavelength sequence; An iteration module executes an iteration algorithm according to the spectral line fitting profile and the spectral line intensity sequence with the plasma parameter to be optimized as an optimization target; An output module is configured to output the optimized plasma parameter.

7. A plasma spectral data parameter optimization apparatus, characterized by, It comprises: a processor and a memory; the processor and the memory are connected through a communication bus; wherein the processor is configured to call and execute a program stored in the memory; the memory is configured to store a program, and the program is at least used to execute the plasma spectrum data parameter optimization method in any one of claims 1-5.

Images